We present DiffTaichi, a new differentiable programming language tailored for
building high-performance differentiable physical simulators. Based on an
imperative programming language, DiffTaichi generates gradients of simulation
steps using source code transformations that preserve arithmetic intensity and
parallelism. A light-weight tape is used to record the whole simulation program
structure and replay the gradient kernels in a reversed order, for end-to-end
backpropagation. We demonstrate the performance and productivity of our
language in gradient-based learning and optimization tasks on 10 different
physical simulators. For example, a differentiable elastic object simulator
written in our language is 4.2x shorter than the hand-engineered CUDA version
yet runs as fast, and is 188x faster than the TensorFlow implementation. Using
our differentiable programs, neural network controllers are typically optimized
within only tens of iterations.

DiffTaichi 是一种新的可微编程语言，专为构建高性能的可微物理模拟器而设计，演示了它在 10 个不同物理模拟器的梯度学习和优化任务中的性能和生产力，可以用于优化神经网络控制器。